Artificial neural network
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This document introduces neural networks and their applications. It discusses how neural networks simulate the human brain using processing nodes and weights to learn from patterns in data. Applications include prediction, pattern detection, and classification. The document also provides an overview of neural network theory, architecture, learning process, and development tools. It notes benefits like handling nonlinear problems and noisy data, as well as limitations such as the "black box" nature and lack of explainability.
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Artificial neural network
- 1. Introduction to Neural Networks And Their Applications Submitted To Dr. Md. Hasanuzzaman Professor,Department of CSE,Dhaka University Submitted By Faria Hossain(181-25-646)
- 2. Table of Contents 1. Introduction of Neural Networks 2. Application of Neural Networks 3. Theory of Neural Networks 4. A Neural Network Demo
- 3. It is simulation of human brain It is the most well known artificial intelligence techniques We are using them: voice recognition system, reading hand writes, door rocks et al. It is a called black box 1.Introduction of Neural Networks
- 4. Neural Networks simulate human brain Learning in Human Brain Neurons Connection Between Neurons Neural Networks As Simulator For Human Brain Processing Elements or Nodes Weights It is a simulator for human brain
- 5. 2. Applications of Neural Networks Prediction of Outcomes Patterns Detection in Data Classification
- 6. Accounting Finance Human Resources Management Marketing Operations Business ANN Applications -1
- 7. Neural Computing is a problem solving methodology that attempts to mimic how human brain functions Artificial Neural Networks (ANN) Machine Learning/Artificial Intelligence 3. Theory of Neural Networks
- 8. The Biological Analogy Neurons: brain cells Nucleus (at the center) Dendrites provide inputs Axons send outputs Synapses increase or decrease connection strength and cause excitation or inhibition of subsequent neurons
- 9. Biological Artificial Soma <-> Node Dendrites <-> Input Axon <-> Output Synapse <-> Weight Artificial Neural Networks (ANN) Three Interconnected Artificial Neurons
- 10. Neural Network Architecture Feed forward Neural Network : Multi Layer Perceptron, - Two, Three, sometimes Four or Five Layers, But normally 3 layers are common structure.
- 11. Step function evaluates the summation of input values Calculating outputs Measure the error (delta) between outputs and desired values Update weights, reinforcing correct results At any step in the process for a neuron, j, we get Delta(Error) = Zj - Yj where Z and Y are the desired and actual outputs, respectively How a Network Learns
- 12. Training A Neural Networks Neural Networks learn from data Learning is finding the best weights values which represent the input and output relationship in Neural Networks (ex: 4*X= 8)-> finding the value for X
- 13. Collect data and separate it into Training set (50%), Testing set (50%) Training set (60%), Testing set (40%) Training set (70%), Testing set (30%) Training set (80%), Testing set (20%) Training set (90%), Testing set (10%) Use training data set to build model Use test data set to validate the trained network training data set and test data set
- 14. Prediction with New Data If the Neural Network's performance in test is good , it can be used to predict outcome of new unseen data If the performance with test is not good, you should collect more data, add more input variables
- 15. Terms in Neural Networks How does Neural Network work for prediction?
- 16. Demo How does Neural Network work for prediction?
- 17. ANN Development Tools E-Miner Clementine NeuroSolutions NeuralWorks Brainmaker PathFinder Trajan Neural Network Simulator NeuroShell Easy NeuroWare
- 18. Benefits of ANN Advantages: Non-linear model leads to better performance It works generally good when data size is small It works generally good when there are noises in data It works generally good when there are missing in data (incomplete data set) Fast decision making Diverse Applications: Pattern recognition Character, speech and visual recognition
- 19. Limitations of ANN Black box that is hardly understood by human Lack of explanation capabilities Training time can be excessive and tedious
- 20. 4. A Neural Networks Demo How do neural networks learn? : trials and errors http://www.youtube.com/watch?v=0 Str0Rdkxxo